了解患者对试验匹配后未实现的试验招生与大型语言模型的匹配
Claire T Verhagen1, Grace Westerman2, Haley Heaviland2
1Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI. Electronic address: https://twitter.com/claireverhagen.
Surgery
|November 29, 2025
概括
一个大型的语言模型平台,OncoLLM-MCW,在外科瘤学中进行自动化临床试验匹配,识别患者入学障碍. 解决这些障碍可以改善癌症临床试验积累.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 临床试验招生对于癌症治疗的进步至关重要.
- 在手术环境中进行临床试验的手动患者识别是低效的.
- 大型语言模型 (LLM) 为自动化临床试验匹配提供了潜力.
研究的目的:
- 评估基于LLM的平台OncoLLM-MCW在将外科瘤患者与临床试验相匹配方面的有效性.
- 从OncoLLM-MCW收到临床试验建议后,确定患者未注册的原因.
主要方法:
- 胃肠道外科瘤诊所患者的回顾性分析 (2024年7月至12月).
- 利用OncoLLM-MCW,一个在机构数据和指导方针上微调的LLM,用于患者试验匹配.
- 评估未实现试验注册的原因作为主要结果.
主要成果:
- 科LLM-MCW评估了514名患者,为32名患者确定了34名潜在的试验匹配.
- 在34个匹配中,只有9个匹配 (26.5%) 导致了患者的注册.
- 不入学的主要原因包括资格不合格 (36%),患者下降 (20%) 和提供者自由裁量权 (16%).
结论:
- 科LLM-MCW成功地为500多名外科瘤患者自动化了临床试验匹配.
- 了解和减轻非注册的原因对于优化癌症临床试验积累至关重要.
- 这种人工智能驱动的方法有可能提高临床试验参与的效率.
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